Picking System Research Articles

Integration of Artificial Vision and Image Processing into a Pick and Place Collaborative Robotic System

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified.

Pigmentation level of human iPSC-derived RPE does not indicate a specific gene expression profile

Retinal pigment epithelium (RPE) cells show heterogeneous levels of pigmentation when cultured in vitro. To know whether their color in appearance is correlated with the function of the RPE, we analyzed the color intensities of human-induced pluripotent stem cell-derived RPE cells (iPSC-RPE) together with the gene expression profile at the single-cell level. For this purpose, we utilized our recent invention, Automated Live imaging and cell Picking System (ALPS), which enabled photographing each cell before RNA-sequencing analysis to profile the gene expression of each cell. While our iPSC-RPE were categorized into four clusters by gene expression, the color intensity of iPSC-RPE did not project any specific gene expression profiles. We reasoned this by less correlation between the actual color and the gene expressions that directly define the level of pigmentation, from which we hypothesized the color of RPE cells may be a temporal condition not strongly indicating the functional characteristics of the RPE.

Pigmentation level of human iPSC-derived RPE does not indicate a specific gene expression profile

Retinal pigment epithelium (RPE) cells show heterogeneous levels of pigmentation when cultured in vitro. To know whether their color in appearance is correlated with the function of the RPE, we analyzed the color intensities of human-induced pluripotent stem cell-derived RPE cells (iPSC-RPE) together with the gene expression profile at the single-cell level. For this purpose, we utilized our recent invention, Automated Live imaging and cell Picking System (ALPS), which enabled photographing each cell before RNA-sequencing analysis to profile the gene expression of each cell. While our iPSC-RPE were categorized into four clusters by gene expression, the color intensity of iPSC-RPE did not project any specific gene expression profiles. We reasoned this by less correlation between the actual color and the gene expressions that directly define the level of pigmentation, from which we hypothesized the color of RPE cells may be a temporal condition not strongly indicating the functional characteristics of the RPE.

Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.

Balanced Order Batching in Bucket Brigade Order Picking Systems with Non-identical Pickers

Balanced Order Batching in Bucket Brigade Order Picking Systems with Non-identical Pickers

Development of Annulus-Object Random Bin Picking System based on Rapid Establishment of RGB-D Images

With the development of the automation industry, robotic arms, and vision applications are no longer limited to fixed actions of the past. Production lines increasingly require the recognition and grasping of objects in complex environments, emphasizing quick setup and stability. In this paper, a rapidly constructed eye-hand system for robotic arm grasping, which enables fast and efficient object manipulation, particularly for stacking, is introduced. Initially, images were captured using a camera to generate extensive datasets from a limited number of images. Objects were subsequently segmented and categorized using deep learning networks for object detection and instance segmentation. Three-dimensional position information was obtained from an RGB-D camera. Finally, object poses were determined based on plane normal vectors, and gripping positions were manually marked. This reduced the time required for grab-point identification, model training, and pose localization. Based on experimental results, the grasping procedure proposed in this paper is suitable for various object-grasping scenarios. It achieved impressive picking success rates of 96% for unstacked annular objects and 90.86% for random bin annular objects, respectively. In the final experiment, following depth information filtering, a success rate of 95.1% was attained with random bin annular object picking.

Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm.

“货到人”拣选系统的两阶段随机规划模型

“货到人”拣选系统的两阶段随机规划模型

Hybrid Genetic Algorithms for Order Assignment and Batching in Picking System: A Systematic Literature Review

Hybrid Genetic Algorithms for Order Assignment and Batching in Picking System: A Systematic Literature Review

Bi-Objective Job-Shop Scheduling Considering Human Fatigue in Cobotic Order Picking Systems: A Case of an Online Grocer

Increasing online retail has resulted in increased automation in order picking systems, leading to new challenges and opportunities in task scheduling. The job-shop scheduling problem is an optimization problem essential in such systems, but existing JSP literature often overlooks workplace fatigue, which harms employees’ well-being and costs U.S. employers up to €127 billion annually. In this work, we propose fatigue consideration in the job-shop scheduling problem in a cobotic order picking system to mitigate its negative effects. We present a new bi-objective mixed integer nonlinear programming problem formulation that considers worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model and scheduling results in a real operation. The mathematical model finds solutions that conventional single-objective optimisation cannot, allowing fractional fatigue distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. The experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements. It also shows a low correlation, meaning it is unfit for application. On the other hand, fatigue-conscious schedules show no clear benefit regarding measured and perceived fatigue. However, the scheduling model could also use heart rate measurements that do not show these inaccuracies. Our study highlights the need to further develop and validate the mathematical formulation and fatigue model and extend to other human factors and indirect fatigue effects.

패션물류센터의 효율적인 B2C 작업 방식에 대한 연구

The fashion industry consists of textile industries that produce and sell textile raw materials clothing industries that manufacture and sell clothing distribution industries that sell fashion consumer goods fashion information fashion publishing and fashion consulting. As the fashion industry gradually moves from offline to online transactions at online shopping malls in the fashion county rose 3.3 percent year-on-year to 49.8158 trillion won in 2022, according to the Seoul Investment Administration. In other words through the pandemic due to COVID-19, the restructuring of fashion distribution channels has accelerated reducing offline distribution networks and rapidly growing online distribution networks. ssfshop, LF, Kolon FnC, and E-Land Group are strengthening their online distribution networks and marketing while Musinsa, W Concept, Zigzag, Able and Brandy which are called the online fashion platform “Big 5,” have “stormous growth.” In this way with the acceleration of online distribution in the fashion industry logistics centers are also establishing an online-only system. However unlike B2B fashion logistics B2C fashion logistics is not easy to achieve logistics operation optimization through automated facilities (Auto Sorter, Digital Picking System (DPS). The reason for this is that B2C fashion logistics manually performs a series of processes such as single/multiple picking of individual units invoice output packaging invoice attachment and delivery preparation according to individual orders of consumers. In addition B2C Fashion Logistics requires work speed and efficiency for rapid and accurate order processing coping with seasonal shipping demand packaging accuracy of skilled personnel and rapid return processing. A study on the efficient working method of B2C fashion logistics was published in 2022 through a study on how to streamline the B2C process of shoe logistics applying the Line Balance methodology. However there was a limit to the fact that the previous paper was not able to conduct comparative analysis with various companies by research and analysis centered on Company A. Therefore this study aims to expand line balancing research in 2022 to introduce the “Flow Shop and Job Shop Method,” which is a work process method and to compare and study work efficiency by applying the Flow Shop and Job Shop methods, focusing on the B2C tasks of Fashion Logistics Center A and B. In addition, this study differs from the meaning of mutual comparison and research on ways to increase work productivity by applying the Flow Shop and Job Shop methods of logistics centers to revitalize online channels in the fashion industry, focusing on the cases of Company A and B.

Impact of Human Energy Expenditure on Order Picking Productivity: A Monte Carlo Simulation Study in a Zone Picking System

Abstract This article aims to investigate the impact of allowable human energy expenditure (HEE) of order pickers on the throughput of workers in manual order zone picking systems MOP. The method used in this research is the Monte Carlo simulation, used while considering many human and job factors. The results showed that a worker’s gender and an item’s weight have little effect on the HEE. On the other hand, body weight, walking speed, distance travelled, and the targeted zone significantly impacted the HEE, rest allowance, and throughput. For example, male pickers at a weight of 75 kg can move up to speed to 1 m/s and pick up items weighing up to 5 kg without reaching the allowable HEE rate, equal to 4.3 kcal/min, and, thus, no rest is needed. Female pickers at a weight of 75 kg reach the allowable HEE rate, equal to 2.6 kcal/min, at a very low speed of approximately 0.1 m/s when picking up items up to 5 kg, and, thus, frequent rest is needed, which leads to low throughput. To increase the throughput of female pickers, they can be assigned to pick up lighter items. Utilising Monte Carlo simulation to evaluate the HEE in MOP while considering many factors.

디지털 피킹 시스템을 위한 연관 분석 기반의 상품 배치

As the e-commerce market has grown rapidly since the coronavirus pandemic, the efficiency of logistics operations has emerged as an important issue. As a way to improve the efficiency of digital picking system (DPS) that handles picking products to fulfill orders, we consider stock keeping unit (SKU) arrangement optimization to reduce the total number of visits to workstations to pack products in totes for a large set of orders. We develop the SKU arrangement optimization model and show that it can be relaxed to be equivalent to multiple-choice knapsack problem (MCKU), which is NP-hard. Recognizing the optimization model cannot be applied in practice, an association-rule-based heuristic algorithm for SKU arrangement is proposed to minimize the number of workstation visits considering variation in the workload among workstations. Under various criteria for SKU selection and arrangement, the heuristic algorithm is shown to significantly reduce the total number of visits not increasing the degree of variation in workstation loads.

A classification approach to order picking systems and policies: Integrating automation and optimization for future research

Picking system optimization is the main condition for improving customer satisfaction and warehouse efficiency. However, related literature does not present a standard classification for the different picking systems and warehouses. This issue implies a lack of scientific uniformity and the difficulty of conducting comparative studies on which the best approaches are, applied to picking problems and/or warehouses. Therefore, this paper aims to provide a theoretical framework on the picking system classifications most used by researchers. The research is descriptive in nature and focuses on the two major categories of picking systems: i) according to automation strategies and processes; ii) according to picking optimization policies and methods. These classifications have been revised so that researchers can combine them with a wide range of picking systems and warehouses. The main contribution of this paper is to provide a better understanding of the main specificities regarding classifications inherent to picking systems and warehouses. We also demonstrate that approaches integrating people, warehouses, technological advances, and adaptation of multi- objective algorithms should be the focus of researchers. The theoretical framework presented corroborates with future studies aimed at managing and adapting picking systems and warehouses. We concluded that integrating picking strategies and policies is critical to dealing with technological advances in warehouses.

An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

Zanthoxylum bungeanum Fruit Detection by Adaptive Thresholds in HSV Space for an Automatic Picking System

Pilot study to determine the concordance between skin prick and intradermal test (IDT) reactivity to environmental allergens in atopic dogs using IDT as the gold standard.

Prick testing is widely used as the first-line in vivo test for environmental allergens in people owing to its noninvasive nature and speed of performance. To determine concordance between skin prick testing (SPT) and intradermal testing (IDT) reactivity to environmental allergen mixes in dogs with atopic dermatitis (cAD). Forty client-owned dogs with cAD. Skin prick testing (GREER Pick System; Stallergenes Greer) and IDT were performed on 40 dogs using seven glycerinated and aqueous environmental allergen mixes, respectively (tree, grass and weed pollens, house dust mites and three mould mixes). Reactions for IDT and SPT were evaluated both subjectively and objectively (mean wheal diameter; MWD) and compared to saline and histamine controls. Using IDT as the gold standard, with subjective scoring, SPT was 47.0% sensitive [95% confidence interval (CI) 36.0%-58.7%], 92.1% specific (95% CI 87.6%-95.3%) and agreement was moderate (79%, Cohen's kappa = 0.424). The positive predictive value of SPT was 36% and negative predictive value was 95%. Objective and subjective scores had only fair agreement. Skin prick testing with allergen mixes was specific yet poorly sensitive as compared to IDT. For both IDT and SPT, 95% (38 of 40) dogs failed to react to an allergen mix, despite showing a positive reaction to at least one component. Future studies comparing SPT and IDT should test individual allergens rather than mixes to prevent the dilution of individual components, which may have resulted in false negatives.

Gantry Robot System Checkers Player

Technological developments in the field of moving goods are increasing rapidly. Production processes in fields such as the automotive, medical world, and even for household needs use the Pick and Place system for the production process. It has been proven, for a long time, that research has improved the Pick and Place system for many sectors, especially how this process can be combined with AI. Because of this, there has been a lot of research on how to make robots play games with their own brains. For now, most of the game development for AI is board games such as chess, Checkers, Tic Tac Toe, and other board games. Therefore, this paper will explain how to make a robot that can play board games. By implementing the gantry robot system, the robot will be made to play the Checkers game by integrating the robot with the existing AI checker. The concept of this robot is to use Checkers AI and machine integration with a camera where the robot will compete with humans, the camera will detect it from the game board, and then with the learning process from AI, the robot can determine where to move the pieces and it is hoped that the motor will move according to the AI output command of the Checkers Robot. After tha,t the robot will return to its original position and wait for its turn.

Design of Intelligent Weighing and Picking System based on Sensing Technology

In recent years, with the development of advanced technology, the replacement of labor by intelligent and automatic equipment has become the main direction of enterprise transformation. In this paper, according to the current epidemic situation, when weighing and packing fresh agricultural products, there are many problems to cope with, like order delay and the non-standard specification and quality, which lead to repeated manual weighing, low efficiency and huge order quantity. Based on the sensing technology, an intelligent weighing and sorting system is designed to solve the problem of terminal sorting. Moreover, by using facilities such as weight sorter and target counterweight machine, the quality of sorted objects can be graded according to specific conveying devices, thus improving the sorting accuracy of agricultural and sideline products, polishing up the end packing efficiency and saving economic costs.

Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System

Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a “parts-to-picker” system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency.